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What is NIXL?
NIXL is an open source library for accelerating point-to-point data transfers in AI inference frameworks. NIXL provides a single, easy-to-use API that can be used to address a variety of data transfer challenges within these frameworks while maintaining maximum performance. This API supports multiple technologies such as RDMA, GPU-initiated networking, GPU-Direct storage, block and file storage, and advanced cloud storage options including S3 over RDMA and Azure Blob Storage. It is vendor-agnostic and can run across diverse environments. For example, it supports Amazon Web Services (AWS) with
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…Pick a “Profiling Target” column and learn what hosts may be used to profile (local or remote) as well as view reports. Profiling Target Linux Workstations & Servers Windows Workstations & Gaming PCs NVIDIA…
CUDA Python
…understand and implement kernel fusion, a technique that combines multiple GPU kernels into a single, more efficient kernel to reduce memory transfers and improve performance. Read: Delivering the Missing Building Blocks for…
코딩 에이전트를 활용한 게임 런타임 추론 비용 최적화 전략
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Achieving Single-Digit Microsecond Latency Inference for Capital Markets | NVIDIA Technical Blog
…Persistent CUDA kernel techniques, green context partitioning, and precomputation phases were key to enabling efficient, low-latency LSTM inference across multiple model instances, with consistent performance when scaling from 1 to 8…
Introducing NVIDIA BlueField-4-Powered CMX Context Memory Storage Platform for the Next Frontier of AI | NVIDIA Technical Blog
…customers adopt DPU‑accelerated, high‑performance, and secure storage networking solutions. Einav has two decades of experience in the storage industry, spanning software development, application engineering, technical marketing, and product management View…
Train Models Faster with JAX and MaxText Using NVFP4 on NVIDIA Blackwell | NVIDIA Technical Blog
…To enable efficient narrow-precision training, the pretraining recipe uses several key techniques that have been chosen based on their performance and accuracy. Five key ingredients work together while maintaining the accuracy…
Scaling Autonomous AI Agents and Workloads with NVIDIA DGX Spark | NVIDIA Technical Blog
…The autotuner will discover optimal tile sizes and occupancy settings for each target architecture automatically, enabling transparent performance portability without any manual configuration. Get started with NVIDIA DGX Spark As AI systems…
cuTile.jl Brings NVIDIA CUDA Tile-Based Programming to Julia | NVIDIA Technical Blog
…Performance of cuTile.jl cuTile.jl targets the same NVIDIA Tile IR backend as cuTile Python, so both packages produce the same kind of GPU machine code. On an NVIDIA GeForce RTX…